Owens

HOWTO: Use NFSv4 ACL

This document shows you how to use the NFSv4 ACL permissions system. An ACL (access control list) is a list of permissions associated with a file or directory. These permissions allow you to restrict access to a certian file or directory by user or group. NFSv4 ACLs provide more specific options than typical POSIX read/write/execute permissions used in most systems.

These commands are useful for managing ACLs in the dir locations of /users/<project-code>.

Understanding NFSv4 ACL

This is an example of an NFSv4 ACL

HOWTO: Reduce Disk Space Usage

This HOWTO will demonstrate how to lower ones' disk space usage. The following procedures can be applied to all of OSC's file systems.

We recommend users regularly check their data usage and clean out old data that is no longer needed.

Users who need assistance lowering their data usage can contact OSC Help.

LAMMPS 14May16 too-many-threads known issue fixed

Date: 
Wednesday, November 30, 2016 - 3:45pm
System(s): 

The LAMMPS 14May16 known issue wherein parallel lammps spawned too many threads has been fixed on all clusters.  No user action is required; if a user had applied the OMP_NUM_THREADS workaround then it may be removed, but it will not cause probems if left in place. The corrected executables were made the defaults for module lammps/14may16 at these times:

Darshan

Darshan is a lightweight "scalable HPC I/O characterization tool".  It is intended to profile I/O by emitting log files to a consistent log location for systems administrators, and also provides scripts to create summary PDFs to characterize I/O in MPI-based programs.

Availability and Restrictions

Versions

The following versions of Darshan are available on OSC clusters:

Spark

Apache Spark is an open source cluster-computing framework originally developed in the AMPLab at University of California, Berkeley but was later donated to the Apache Software Foundation where it remains today. In contrast to Hadoop's disk-based analytics paradigm, Spark has multi-stage in-memory analytics. Spark can run programs up-to 100x faster than Hadoop’s MapReduce in memory or 10x faster on disk. Spark support applications written in python, java, scala and R

Changes of Default Memory Limits

Problem Description

Our current GPFS file system is a distributed process with significant interactions between the clients. As the compute nodes being GPFS flle system clients, a certain amount of memory of each node needs to be reserved for these interactions. As a result, the maximum physical memory of each node allowed to be used by users' jobs is reduced, in order to keep the healthy performance of the file system. In addition, using swap memory is not allowed.  

The table below summarizes the maximum physical memory allowed for each type of nodes on our systems:

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